import mne
from pathlib import Path
import matplotlib
import numpy as np
from autoreject import AutoReject
from autoreject import get_rejection_threshold
import autoreject
mne.viz.set_browser_backend("matplotlib", verbose=None)Using matplotlib as 2D backend.counter = 0
# number of set files
p = Path("/Volumes/T7/BrainLat")
set_list = list(p.glob("**/*.set"))
print(len(set_list))
my_list = [file for file in set_list if file.parent.name != "3_PD"]104Some text
data_path = "/Volumes/T7/Brainlat_MNE/data"
for file in my_list:
raw = mne.io.read_raw_eeglab(file, preload=True).set_montage("biosemi128")
# raw.compute_psd(method="welch")
filter_params = {
"l_freq": 1,
"h_freq": 45,
"h_trans_bandwidth": 5,
"fir_design": "firwin2",
"phase": "zero-double",
}
raw_filtered = raw.filter(**filter_params) # removedrifts
events = mne.make_fixed_length_events(
raw=raw_filtered, start=0, stop=None, duration=3, overlap=0, first_samp=False
)
# Create Epochs object
epochs = mne.Epochs(
raw=raw_filtered,
events=events,
tmin=0,
tmax=3,
detrend=0,
baseline=None,
preload=True,
)
# epochs = mne.make_fixed_length_epochs(raw, duration=3, preload=True)
# display(epochs_plot)
epochs_plot = epochs.average().detrend().plot_joint()
# display(psd_plot)
ar = autoreject.AutoReject(
n_interpolate=[
1,
2,
4,
8,
16,
32,
64,
128,
],
random_state=11,
n_jobs=-1,
verbose=True,
)
ar.fit(epochs) # fit on a few epochs to save time
epochs_ar, reject_log = ar.transform(epochs, return_log=True)
try:
epochs[reject_log.bad_epochs].plot(scalings=dict(eeg=100e-6))
except Exception as e:
print(f"An error occurred: {e}")
rejected_plot = reject_log.plot("horizontal", show=False)
epochs_ar.save(f"{data_path}/epochs/{file.stem}-epo.fif")
epo_spectrum = epochs_ar.compute_psd(
method="welch", picks="eeg", exclude="bads")
psd_plot = epo_spectrum.plot(
average=True, picks="eeg", exclude="bads", show=False)
epo_spectrum.plot_topomap(ch_type="eeg", agg_fun=np.median)
epo_spectrum.save(f"{data_path}/psd/{file.stem}.h5", overwrite=True)Filtering raw data in 1 contiguous segment
Setting up band-pass filter from 1 - 45 Hz
FIR filter parameters
---------------------
Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:
- Windowed frequency-domain design (firwin2) method
- Hamming window
- Lower passband edge: 1.00
- Lower transition bandwidth: 1.00 Hz (-12 dB cutoff frequency: 0.50 Hz)
- Upper passband edge: 45.00 Hz
- Upper transition bandwidth: 5.00 Hz (-12 dB cutoff frequency: 47.50 Hz)
- Filter length: 3380 samples (6.602 s)
Not setting metadata
139 matching events found
No baseline correction applied
0 projection items activated
Using data from preloaded Raw for 139 events and 1537 original time points ...
0 bad epochs dropped
No projector specified for this dataset. Please consider the method self.add_proj.
Running autoreject on ch_type=eeg
Estimated consensus=0.30 and n_interpolate=32
No bad epochs were found for your data. Returning a copy of the data you wanted to clean. Interpolation may have been done.
An error occurred: epochs.plot() can't run because this Epochs-object is empty. You might want to check Epochs.drop_log or Epochs.plot_drop_log() to see why epochs were dropped.
Effective window size : 3.002 (s)
Averaging across epochs...
Overwriting existing file.
Filtering raw data in 1 contiguous segment
Setting up band-pass filter from 1 - 45 Hz
FIR filter parameters
---------------------
Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:
- Windowed frequency-domain design (firwin2) method
- Hamming window
- Lower passband edge: 1.00
- Lower transition bandwidth: 1.00 Hz (-12 dB cutoff frequency: 0.50 Hz)
- Upper passband edge: 45.00 Hz
- Upper transition bandwidth: 5.00 Hz (-12 dB cutoff frequency: 47.50 Hz)
- Filter length: 3380 samples (6.602 s)
Not setting metadata
123 matching events found
No baseline correction applied
0 projection items activated
Using data from preloaded Raw for 123 events and 1537 original time points ...
0 bad epochs dropped
No projector specified for this dataset. Please consider the method self.add_proj.
Running autoreject on ch_type=eeg
Estimated consensus=0.20 and n_interpolate=16
No bad epochs were found for your data. Returning a copy of the data you wanted to clean. Interpolation may have been done.
An error occurred: epochs.plot() can't run because this Epochs-object is empty. You might want to check Epochs.drop_log or Epochs.plot_drop_log() to see why epochs were dropped.
Effective window size : 3.002 (s)
Averaging across epochs...
Overwriting existing file.[Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.3s
[Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.2s
data_path = "/Volumes/T7/Brainlat_MNE/data"
epo_spectrum.save(f"{data_path}/psd/{file.stem}.h5")epo_spectrum.save(f"~/PhD/Projects/brain-lat/data/psd/{file.stem}.h5")ica = mne.preprocessing.ICA(random_state=99, n_components=60, method="picard")
ica.fit(epochs_ar)Fitting ICA to data using 128 channels (please be patient, this may take a while)
Selecting by number: 60 components
Fitting ICA took 35.5s.| Method | picard |
| Fit parameters | max_iter=500 |
| Fit | 225 iterations on epochs (189051 samples) |
| ICA components | 60 |
| Available PCA components | 128 |
| Channel types | eeg |
| ICA components marked for exclusion | — |
_ = ica.plot_sources(
epochs_ar, show_scrollbars=False, start=0, stop=60, picks=slice(0, 19)
)
_ = ica.plot_sources(
epochs_ar, show_scrollbars=False, start=0, stop=60, picks=slice(20, 39)
)
_ = ica.plot_sources(
epochs_ar, show_scrollbars=False, start=0, stop=60, picks=slice(40, 59)
)Not setting metadata
123 matching events found
No baseline correction applied
0 projection items activated
Not setting metadata
123 matching events found
No baseline correction applied
0 projection items activated
Not setting metadata
123 matching events found
No baseline correction applied
0 projection items activated
